qdrant-scaling-data-volume
Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.
git clone --depth 1 https://github.com/qdrant/skills /tmp/qdrant-scaling-data-volume && cp -r /tmp/qdrant-scaling-data-volume/skills/qdrant-scaling/scaling-data-volume ~/.claude/skills/qdrant-scaling-data-volumeSKILL.md
# Scaling Data Volume This document covers data volume scaling scenarios, where the total size of the dataset exceeds the capacity of a single node. ## Tenant Scaling If the use case is multi-tenant, meaning that each user only has access to a subset of the data, and we never need to query across all the data, then we can use multi-tenancy patterns to scale. The recommended way is to use multi-tenant workloads with payload partitioning, per-tenant indexes, and tiered multitenancy. Learn more [Tenant Scaling](tenant-scaling/SKILL.md) ## Sliding Time Window Some use-cases are based on a sliding time window, where only the most recent data is relevant. For example an index for social media posts, where only the last 6 months of data require fast search. Learn more [Sliding Time Window](sliding-time-window/SKILL.md) ## Global Search Most general use-cases require global search across all data. In these situations, we might need to fall back to vertical scaling, and then horizontal scaling when we reach the limits of vertical scaling. ### Vertical Scaling When data doesn't fit in a single node, the first approach is to scale the node itself — more RAM, better disk, quantization, mmap. Exhaust vertical options before going horizontal, as horizontal scaling adds permanent operational complexity. Learn more [Vertical Scaling](vertical-scaling/SKILL.md) ### Horizontal Scaling When a single node can't hold the data even with quantization and mmap, distribute data across multiple nodes via sharding. Learn more [Horizontal Scaling](horizontal-scaling/SKILL.md)
Qdrant provides client SDKs for various programming languages, allowing easy integration with Qdrant deployments.
Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
Guides Qdrant monitoring and observability setup. Use when someone asks 'how to monitor Qdrant', 'what metrics to track', 'is Qdrant healthy', 'optimizer stuck', 'why is memory growing', 'requests are slow', or needs to set up Prometheus, Grafana, or health checks. Also use when debugging production issues that require metric analysis.
Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.
Guides Qdrant monitoring setup including Prometheus scraping, health probes, Hybrid Cloud metrics, alerting, and log centralization. Use when someone asks 'how to set up monitoring', 'Prometheus config', 'Grafana dashboard', 'health check endpoints', 'how to scrape Hybrid Cloud', 'what alerts to set', 'how to centralize logs', or 'audit logging'.
Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
Diagnoses and fixes slow Qdrant indexing and data ingestion. Use when someone reports 'uploads are slow', 'indexing takes forever', 'optimizer is stuck', 'HNSW build time too long', or 'data uploaded but search is bad'. Also use when optimizer status shows errors, segments won't merge, or indexing threshold questions arise.